- getMenuItemsJson — UNCHANGED
{
"nodes": [
{
"id": "ribeye-id",
"itemId": "pg-item-ribeye",
"itemModifierId": "pg-im-ribeye",
| ! 2022-02-28 https://www.quora.com | |
| www.quora.com##[class*="blocking_wall"] | |
| www.quora.com##*:style(filter: none !important) | |
| www.quora.com##.qu-overflow--hidden:style(overflow:auto!important) |
{
"nodes": [
{
"id": "ribeye-id",
"itemId": "pg-item-ribeye",
"itemModifierId": "pg-im-ribeye",
\documentclass[12pt]{article} \usepackage[utf8]{inputenc} \usepackage{amsmath} \usepackage{graphicx} \usepackage{hyperref}
\title{Photonic Universe Hypothesis (PUH) v6: A Theory of Everything} \author{Brian Martell} \date{August 10, 2025}
Comment out this line from your VMX file: sound.virtualDev = "hdaudio"
If you don't want to do that, change the audio format settings in Windows to "24 bit, 44100 Hz (Studio Quality)" or higher, which greatly improves (※ but does not completely fix) the issue.
See this for more info: https://kb.vmware.com/s/article/2012007
| /*** | |
| * Clash Verge Rev 全局扩展脚本(懒人配置)/ Mihomo Party 覆写脚本 | |
| * URL: https://gist.github.com/dahaha-365/0b8beb613f8d1ee656fe1f21e1a07959 | |
| */ | |
| /** | |
| * 整个脚本的总开关,在Mihomo Party使用的话,请保持为true | |
| * true = 启用 | |
| * false = 禁用 | |
| */ |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.